Earthquake Notebook

Earthquake Analysis

Setup

Libraries

library(tidyverse)
library(RCurl)
library(plotly)
library(data.table)
library(DT)

Data

Static Datasets

cache data in notebook to have access to most recent when not connect to internet

dl <- getURL('https://www.engineeringbigdata.com/wp-content/uploads/earthquake_all.csv')
gEQdata <- read_csv(file = dl)

Explore Data

summary(gEQdata, na.rn=T)

##       time                        latitude        longitude     
##  Min.   :2017-12-26 21:19:32   Min.   :-62.51   Min.   :-180.0  
##  1st Qu.:2018-01-03 04:26:26   1st Qu.: 35.66   1st Qu.:-149.5  
##  Median :2018-01-10 07:10:53   Median : 38.82   Median :-121.1  
##  Mean   :2018-01-10 05:05:26   Mean   : 42.27   Mean   :-118.0  
##  3rd Qu.:2018-01-17 01:01:44   3rd Qu.: 58.19   3rd Qu.:-116.8  
##  Max.   :2018-01-25 21:02:41   Max.   : 84.66   Max.   : 180.0  
##                                                                 
##      depth             mag           magType               nst        
##  Min.   : -3.26   Min.   :-0.820   Length:9243        Min.   :  0.00  
##  1st Qu.:  3.82   1st Qu.: 0.700   Class :character   1st Qu.:  8.00  
##  Median :  8.30   Median : 1.200   Mode  :character   Median : 14.00  
##  Mean   : 23.61   Mean   : 1.486                      Mean   : 18.74  
##  3rd Qu.: 17.30   3rd Qu.: 1.900                      3rd Qu.: 23.00  
##  Max.   :666.46   Max.   : 7.900                      Max.   :289.00  
##                   NA's   :1                           NA's   :3743    
##       gap             dmin              rms             net           
##  Min.   : 13.0   Min.   : 0.0001   Min.   :0.0000   Length:9243       
##  1st Qu.: 73.0   1st Qu.: 0.0184   1st Qu.:0.0900   Class :character  
##  Median :109.0   Median : 0.0556   Median :0.1800   Mode  :character  
##  Mean   :124.8   Mean   : 0.4992   Mean   :0.2973                     
##  3rd Qu.:163.0   3rd Qu.: 0.1500   3rd Qu.:0.4600                     
##  Max.   :356.0   Max.   :37.2230   Max.   :4.1200                     
##  NA's   :2901    NA's   :2953      NA's   :3                          
##       id               updated                       place          
##  Length:9243        Min.   :2017-12-26 22:20:20   Length:9243       
##  Class :character   1st Qu.:2018-01-08 17:59:25   Class :character  
##  Mode  :character   Median :2018-01-13 02:32:21   Mode  :character  
##                     Mean   :2018-01-13 17:03:22                     
##                     3rd Qu.:2018-01-19 23:05:28                     
##                     Max.   :2018-01-25 21:15:05                     
##                                                                     
##      type           horizontalError   depthError          magError     
##  Length:9243        Min.   : 0.00   Min.   :    0.00   Min.   :0.0000  
##  Class :character   1st Qu.: 0.25   1st Qu.:    0.42   1st Qu.:0.0990  
##  Mode  :character   Median : 0.42   Median :    0.78   Median :0.1560  
##                     Mean   : 1.75   Mean   :   12.46   Mean   :0.1881  
##                     3rd Qu.: 1.10   3rd Qu.:    1.90   3rd Qu.:0.2330  
##                     Max.   :42.52   Max.   :45969.50   Max.   :4.4500  
##                     NA's   :3974    NA's   :3          NA's   :3127    
##      magNst         status          locationSource      magSource        
##  Min.   :  0.0   Length:9243        Length:9243        Length:9243       
##  1st Qu.:  4.0   Class :character   Class :character   Class :character  
##  Median :  8.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 16.4                                                           
##  3rd Qu.: 18.0                                                           
##  Max.   :551.0                                                           
##  NA's   :2971

Statistics

sum <- summary(gEQdata$mag, na.rm=T)
sum

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -0.820   0.700   1.200   1.486   1.900   7.900       1

Magnitude

Summary statistics for Global Earthquake Dataset Maximum: 7.9 Minimum: -0.82

Median: 1.2 Mean: 1.4855085

Reduce Dataset

According to UPSeis an Earthquake with a magnitude of greater than 6 is significant

Sig6Quake <- gEQdata[gEQdata$mag > 6,]
Sig6sum <- summary(Sig6Quake$mag, na.rm=T)
Sig5Quake <- gEQdata[gEQdata$mag > 5,]
Sig5sum <- summary(Sig5Quake$mag, na.rm=T)
Sig5sum

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   5.100   5.100   5.300   5.463   5.600   7.900       1

Earthquake Magnitude Classes

# sort classes
greatcnt <- count(gEQdata[gEQdata$mag>=8,])
majorcnt <- count(gEQdata[gEQdata$mag>=7,])
strongcnt <- count(gEQdata[gEQdata$mag>=6,])
moderatecnt <- count(gEQdata[gEQdata$mag>=5,])
lightcnt <- count(gEQdata[gEQdata$mag>=4,])
minorcnt <- count(gEQdata)

magClasses <- data.table(Class = 
                            c("Great","Major","Strong","Moderate",
                              "Light","Minor"),
                        Magn = 
                            c("8 or more","7 - 7.9","6 - 6.9","5 - 5.9",
                              "4 - 4.9","3.9 and under"),
                        Est_Each_Year =
                            c("one every 5 to 10 years","20","100",
                              "500","50,000","990,000"),
                        Est_Each_Month =
                            c("0","1.5","8.3","41.6","2,500","75,000"),
                        This_Month =
                            c(greatcnt,majorcnt,strongcnt,moderatecnt,
                              lightcnt,minorcnt))

names(magClasses)[2] <- "Magnitude"
names(magClasses)[3] <- "Estimated Each Year"
names(magClasses)[4] <- "Estimated Each Month"
names(magClasses)[5] <- "This Month"

datatable(magClasses, rownames = F)

The numbers on this table show us that the month from December 26m 2017 until January 25, 2018 has fewer Minor class earthquakes than normal.
There are more Great class,
4 times as many Major Class,
and over 10% more Strong class Earthquakes.

Fit given points of Magnitude and Number of Earthquakes

mag <- c(2.5,5.5,6.1,7.0,8.0)

count <- c(900000,30000,500,100,20)

dt <- data.table(mag, count)

p <- plot_ly(dt, x = ~mag) %>% 
    add_markers(y = ~count)

pl <- p %>% add_lines(y= ~fitted(loess(count ~ mag)))

pl

Visualize Data

Bubble Map

Plotly Bubble Map

# use Magnitude 5 and above events
Sig5Quake <- gEQdata[gEQdata$mag > 5,]
names(Sig5Quake)[2] <- "lat"
names(Sig5Quake)[3] <- "lon"

g <- list(
  #scope = 'usa',
  #projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray85"),
  subunitwidth = 1,
  countrywidth = 1,
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white")
)

p <- plot_geo(Sig5Quake, locationmode = 'USA-states', sizes = c(1, 250)) %>%
  add_markers(
    x = ~lon, y = ~lat, size = ~mag, color = ~magError, hoverinfo = "text",
    text = ~paste(Sig5Quake$mag, "Magnitude","<br>",
                  Sig5Quake$place, "<br />",
                  Sig5Quake$time)
  ) %>%
  layout(title = 'Dec 26, 2017 to January 25, 2018 Earthquakes over Magnitude 5', geo = g)

p

## Warning: Ignoring 1 observations

## Warning: `line.width` does not currently support multiple values.